{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('y_train: ', (49000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n",
      "('y_val: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984772881e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  1.09081995087e-10\n",
      "dw error:  2.17526355046e-10\n",
      "db error:  7.73697883449e-12\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  6.39553504205e-11\n",
      "dw error:  8.16201110576e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9996027491\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3025458445\n",
      "dx error:  9.38467316199e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.22e-08\n",
      "W2 relative error: 3.50e-10\n",
      "b1 relative error: 8.37e-09\n",
      "b2 relative error: 2.53e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 7.98e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.301434\n",
      "(Epoch 0 / 10) train acc: 0.164000; val_acc: 0.134000\n",
      "(Iteration 101 / 4900) loss: 1.824051\n",
      "(Iteration 201 / 4900) loss: 1.961900\n",
      "(Iteration 301 / 4900) loss: 1.613144\n",
      "(Iteration 401 / 4900) loss: 1.495818\n",
      "(Epoch 1 / 10) train acc: 0.450000; val_acc: 0.451000\n",
      "(Iteration 501 / 4900) loss: 1.563394\n",
      "(Iteration 601 / 4900) loss: 1.443666\n",
      "(Iteration 701 / 4900) loss: 1.604075\n",
      "(Iteration 801 / 4900) loss: 1.605911\n",
      "(Iteration 901 / 4900) loss: 1.408392\n",
      "(Epoch 2 / 10) train acc: 0.480000; val_acc: 0.464000\n",
      "(Iteration 1001 / 4900) loss: 1.455671\n",
      "(Iteration 1101 / 4900) loss: 1.468152\n",
      "(Iteration 1201 / 4900) loss: 1.380462\n",
      "(Iteration 1301 / 4900) loss: 1.280830\n",
      "(Iteration 1401 / 4900) loss: 1.445764\n",
      "(Epoch 3 / 10) train acc: 0.503000; val_acc: 0.479000\n",
      "(Iteration 1501 / 4900) loss: 1.384686\n",
      "(Iteration 1601 / 4900) loss: 1.332996\n",
      "(Iteration 1701 / 4900) loss: 1.341420\n",
      "(Iteration 1801 / 4900) loss: 1.456930\n",
      "(Iteration 1901 / 4900) loss: 1.427660\n",
      "(Epoch 4 / 10) train acc: 0.504000; val_acc: 0.487000\n",
      "(Iteration 2001 / 4900) loss: 1.425840\n",
      "(Iteration 2101 / 4900) loss: 1.389251\n",
      "(Iteration 2201 / 4900) loss: 1.458540\n",
      "(Iteration 2301 / 4900) loss: 1.190365\n",
      "(Iteration 2401 / 4900) loss: 1.231720\n",
      "(Epoch 5 / 10) train acc: 0.529000; val_acc: 0.481000\n",
      "(Iteration 2501 / 4900) loss: 1.311956\n",
      "(Iteration 2601 / 4900) loss: 1.257376\n",
      "(Iteration 2701 / 4900) loss: 1.255261\n",
      "(Iteration 2801 / 4900) loss: 1.359596\n",
      "(Iteration 2901 / 4900) loss: 1.252704\n",
      "(Epoch 6 / 10) train acc: 0.563000; val_acc: 0.512000\n",
      "(Iteration 3001 / 4900) loss: 1.141624\n",
      "(Iteration 3101 / 4900) loss: 1.041271\n",
      "(Iteration 3201 / 4900) loss: 1.537659\n",
      "(Iteration 3301 / 4900) loss: 1.195672\n",
      "(Iteration 3401 / 4900) loss: 1.411472\n",
      "(Epoch 7 / 10) train acc: 0.547000; val_acc: 0.494000\n",
      "(Iteration 3501 / 4900) loss: 1.193842\n",
      "(Iteration 3601 / 4900) loss: 1.033797\n",
      "(Iteration 3701 / 4900) loss: 1.246135\n",
      "(Iteration 3801 / 4900) loss: 1.203015\n",
      "(Iteration 3901 / 4900) loss: 0.929300\n",
      "(Epoch 8 / 10) train acc: 0.545000; val_acc: 0.502000\n",
      "(Iteration 4001 / 4900) loss: 1.089804\n",
      "(Iteration 4101 / 4900) loss: 1.174137\n",
      "(Iteration 4201 / 4900) loss: 1.052676\n",
      "(Iteration 4301 / 4900) loss: 0.998013\n",
      "(Iteration 4401 / 4900) loss: 1.530019\n",
      "(Epoch 9 / 10) train acc: 0.594000; val_acc: 0.513000\n",
      "(Iteration 4501 / 4900) loss: 1.118980\n",
      "(Iteration 4601 / 4900) loss: 1.280910\n",
      "(Iteration 4701 / 4900) loss: 1.336721\n",
      "(Iteration 4801 / 4900) loss: 1.083075\n",
      "(Epoch 10 / 10) train acc: 0.583000; val_acc: 0.476000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model, data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=10, batch_size=100,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8sOSams6f+D5gKPAV4GKg2KMsUChMY8eOBSxZsqo7H0GSJEmSJEnqUHcryy4DaoAfALtL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4IsI93s9PT0TFzd8BjrGf3ri9QEw65FuZBfOjiyn/xL0DfJNJGU57ki7el0zh1MfKDs9BOd8rWN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Wj0lwWeZumFIjtWsXLFlScWps27b4ud6946XYTTfFi7Gzz4b09GjzJoNj3jGyrKT8nvSU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x49035f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30047908977\n",
      "W1 relative error: 1.48e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 3.53e-07\n",
      "b1 relative error: 5.38e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 5.80e-11\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.05211477653\n",
      "W1 relative error: 7.36e-09\n",
      "W2 relative error: 6.87e-08\n",
      "W3 relative error: 3.48e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 1.80e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.375113\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.097000\n",
      "(Epoch 1 / 20) train acc: 0.260000; val_acc: 0.096000\n",
      "(Epoch 2 / 20) train acc: 0.440000; val_acc: 0.138000\n",
      "(Epoch 3 / 20) train acc: 0.540000; val_acc: 0.121000\n",
      "(Epoch 4 / 20) train acc: 0.640000; val_acc: 0.163000\n",
      "(Epoch 5 / 20) train acc: 0.600000; val_acc: 0.158000\n",
      "(Iteration 11 / 40) loss: 1.278468\n",
      "(Epoch 6 / 20) train acc: 0.820000; val_acc: 0.171000\n",
      "(Epoch 7 / 20) train acc: 0.780000; val_acc: 0.171000\n",
      "(Epoch 8 / 20) train acc: 0.800000; val_acc: 0.157000\n",
      "(Epoch 9 / 20) train acc: 0.940000; val_acc: 0.201000\n",
      "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.180000\n",
      "(Iteration 21 / 40) loss: 0.437798\n",
      "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.180000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.204000\n",
      "(Epoch 13 / 20) train acc: 0.960000; val_acc: 0.169000\n",
      "(Epoch 14 / 20) train acc: 0.980000; val_acc: 0.175000\n",
      "(Epoch 15 / 20) train acc: 0.980000; val_acc: 0.196000\n",
      "(Iteration 31 / 40) loss: 0.085872\n",
      "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.187000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.177000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.192000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.175000\n",
      "(Epoch 20 / 20) train acc: 0.980000; val_acc: 0.168000\n"
     ]
    },
    {
     "data": {
      "image/png": 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QkEAFAADQkEAFAADQkEAFAADQkEA1BGqtgy4BAABoQKAakE6nk8nJwxkf35nNm+/O+PjO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\n1pxUkJa2ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x496e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 8e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 139.150523\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.087000\n",
      "(Epoch 1 / 20) train acc: 0.180000; val_acc: 0.110000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.125000\n",
      "(Epoch 3 / 20) train acc: 0.520000; val_acc: 0.137000\n",
      "(Epoch 4 / 20) train acc: 0.560000; val_acc: 0.123000\n",
      "(Epoch 5 / 20) train acc: 0.680000; val_acc: 0.130000\n",
      "(Iteration 11 / 40) loss: 2.793172\n",
      "(Epoch 6 / 20) train acc: 0.820000; val_acc: 0.133000\n",
      "(Epoch 7 / 20) train acc: 0.920000; val_acc: 0.147000\n",
      "(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.149000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.149000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.150000\n",
      "(Iteration 21 / 40) loss: 0.001506\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.149000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Iteration 31 / 40) loss: 0.000920\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.147000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.147000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.148000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.148000\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zwms6ySVJfrTPEAEAAJaPhUTWvZI8ZI7tD0ly7Ojrb2a4rBAAAOCospDIekuS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\nIhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd6a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-1\n",
    "learning_rate = 1e-3\n",
    "\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]\n",
    "While I tuning the hyperparameters of the five layer, I find the min loss is more sensitive to the weight_scale which make the loss function converge to a local minimum easily. I think this phenomenon happened beacause the high capacity of the five layer also make the five layer net's loss function more complex and harder to optimal domain than the three layer net and much more sensitive to initialization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  }
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